Dylan Patel: GPT-5, NVIDIA, Intel, Meta, Apple

22:55For a while. It's no longer, right? The guys that build services on top, like Google or Amazon or Meta eventually eclipsed. Which is why Nvidia is making all the software libraries, right? And they're trying to commoditize inference, right? You guys don't, I think, even have an inference API provider investment, do you? Well, with all kinds of model providers. Model -Coviders. I'm talking about a pure API provider investment. I think, right, is that correct? I think I talked to one of the team members, maybe Rajko or someone about why you guys didn't invest in a fireworks. And the argument was, well, we think just serving models alone without making them will sort of be commoditized.

23:38Yeah. We have some in the CBTF system, with like a file. Without a decay. Yeah, it's a little bit different dynamics there, I think. They tend to make much more component models than the OM folks. I think they're different. Yeah, absolutely. But you guys don't have one of these like, you know, base 10 or any of these like sort of like API investments because you think this is from someone on the infrared team that you guys think it'll get commoditized because of software and video making because VLM and S .D. Laying, which is like open source software coming out of Berkeley and now sort of has their own environments now and supported by many like this being commoditized means that like API the I providers aren't necessarily worth a ton, right?

24:18Is sort of your argument, maybe. I think that's relevant to this whole thing, which is, you know, why, right? Like, why would you do this? Shifting gears. What about the Silicon startups? What's your take on those? I mean, there's a ton of capital flowing into that, where I've seen, I have not a number, it's probably billions being invested in and ship startups. Yeah, for sure, for sure. I mean, like, whether you're looking at like, you know, companies like, I think it's pretty impressive that a few companies like Etch and Revos and a number of other companies, you know, Maddox and others have gotten the amount of funding they've had without even launching a chip, right?

24:58You know, in the past, like, yes, Silicon companies would make money or raise money, but they would at least launch a chip before they get a, you know, a big round, but like Etch and Revos have raised, you know, a lot of money without ever launching a chip publicly, which I think is, I mean, it speaks to, Well, yes, Silicon is super capital intensive if you're building a chip, especially an accelerator, which has so many moving pieces. And there's like 10 different AI accelerator companies out there, right? That are new -ish in the last few years. It gives a lot more. That are like, yeah, that's fair.

25:32And then there's the old guard which continues to raise money, right? Like Grockens, Rebrests, and Sominova, and Tens Torren, and so on and so forth, right? like, or Graph Quirking bought out by Softbank and Softbank dumping money into this effort as well, right? There's a lot of capital being invested to disperse, to spell sort of Nvidia's top dollar or top position. But it becomes challenging, right? It's like, how do you beat Nvidia, right? Like, the hyper scalers, I think, are like kind of lucky in that. They can do mostly the same thing as Nvidia, right? They've got a couple of customers, which is themselves.

26:07Sorry, that's a huge estimate. And it's, they can just win on supply chain, right? I'm using cheaper providers. It's a margin compression exercise, essentially. Yeah, yeah. And maybe for certain workloads, like metaphor recommendation systems, they'll have a better, they can specialize more. But for the most part, it's like, no, we're targeting the same workloads. We can just simplify supply chain or in house a lot of it and compress margin. And it'll be fine. But in the case of these other companies, it's like, well, they don't have a captive customer. So now you have to contend with, well, I'm using the same ecosystem.

26:39them. And either I can use some custom silicon provider who's going to take a margin anyways on top and that's going to compress my like what I can sell for or I can try and in house everything. But then it's like, this is really hard, right? Like I'm going to do all the software design. I'm going to do all the silicon design. I'm going to build all this different IP. I'm going to manage the supply chain on chips on racks on everything, right? Ends up being a huge effort in terms of team size, all in the end, like, hey, I make a 75 % gross margin as Nvidia, AMD sells their GPUs for 50 % gross margin, and they have a hard time out engineering Nvidia, and they're great at engineering, right?

27:19But yet, they still take more silicon area, more memory to achieve the same performance, and they have to sell for less, so their margin gets compressed. That makes sense. I think, historically, if you look at it, But typically, if a new entrance in markets didn't win by marginally improving on something existing, they happen sometimes. But more likely, they jumped up some kind of disruptive technology leap. Right? Whereas we have a different approach, we have different technology. Is that possible here? I mean, to some degree, maybe there's over -fantons simplifying a little bit, but I think part of the reason why the transformer model won was because it runs so incredibly great on GPUs.

27:54Like a recurrent neural network is similarly performing it looks like, but it runs terribly on now on a GPU. So did we sort of pick the model for an architecture? And now it's hard to come up with an architecture that, you know, really... Well, it's hardware software's design, right? Like, there's all this hype about neuro -morphic computing, right? Like, theoretically, it's amazing and super efficient. It's like, okay, great. Like, there's no ecosystem of hardware. There's no ecosystem of software. It would take like, you know, tens of thousands of people who are the best AI today, focusing on that to even prove out if it's worthwhile or not, right?

28:27on a hardware side, on a software side, on a model side. And so like you look at like Grox or Rebrace, Samanova, they all sort of over indexed to the models that were leading at the time when they designed their chips. And so they made certain trade -offs, right? They put a lot more memory on chip. And Nvidia was like, well, we're not gonna do that. That's faster at least, right? Well, more like if you could bring the amount of memory of SRAM on Nvidia's chips, it's much, much lower. Yes, correct. Then when SRAM instead of DRAM, but then they usually have less DRAM. So there's a trade -off there as well.

28:56There's less DRM, there's more SRM, and because there's more SRM on the chip, you have to have less compute on the chip, and so they ended up losing, right? Because the model size is got too big and all this, right? And so you have this like super weird dynamic where they bet on something that was actually better, right? Like I have no doubt that cerebris would run certain types of models better than Nvidia or GROC, or hey, Dojo, right? Dojo runs certain, you know, in Tesla's Dojo, would run certain types of models way better than Nvidia's chips. because they're optimized to that. But then it's like, oh, well, actually, even envisioned has to use vision transformers now.

29:31So it's like, okay, cool. Gives model sizes grew and all these things. So it ends up being a, you know, catch 22 and that like you optimize for something. And so now like today, you have this new age of AI accelerator companies are like, okay, we're going to optimize for transformers. But the time they started designing, they're like, okay, transformers are dense models that are this big. What's the best, you know, the hidden dimension is 8K and your back sizes are this big and your sequence ones are this big. So let's just make a super large systolic array. So you can create the maximum efficiency and that turns out, oh look at deep seek or go look at what the labs are doing.

30:04Actually, there are shapes are much smaller. Actually, you need to do a bunch of small matrix multiplies, not massive, massive, massive, you know, singular matrix multiplies per layer. And then it ends up, you know, oh, well, that chip you're designing for that is actually not super effective for that. And so the software is evolving constantly because of what, because of what works best on Nvidia. and you see that with, you know, whether it be what Deep Seeks doing or other Bobbos doing, or what the labs are doing internally. And you even see this like for Google, right? Like their open source Gemma models make different decisions because the shapes of a TPU are different than a GPU.

30:41And those, the GPU and the TPU are actually not that far apart, right? Like you would say yes, they're very different, but like Blackwell and TPUs are very, very, they're converging on similar designs, actually. Whereas, to be in video, you can't just have this supply chain win. You don't have this captive customer. So now you need to do something that will give you 5X advantage in hardware efficiency for a certain type of workload. And then pray the workload doesn't shift. Because in videos also optimizing their architecture generation, they've added a lot of stuff to make their chips way better for the existing models.

31:16but it's like they're taking large steps every year, every two years towards something, whereas you have to go way over there and left field and hope that models stay over there, right? Because you have to win by 5x because Nvidia is gonna have supply chain efficiency over you. They're gonna have time to market over you in terms of like a new process, note, or new memory, or whatever technology, right? Even AMD, right? They got to two nanometer before Nvidia. They had higher density HBM. They use 3D stacking. All these things on supply chain that should be better than Nvidia, and yet they still lose.

31:51They're still the software angle. I mean Nvidia is fantastic. Yeah, and then there's software as well, right? But it's like Nvidia's going to have better networking than you. They're going to have better HBM. They're going to have better process node. They're going to come to market faster. They're going to be able to ramp faster. They're going to have better negotiations with whether it's TSMC or SK high nicks in the memory and silicon side or all the racked people or like copper cables. Everything they're going to have better cost efficiency. So you have to be like 5x better. But to be fair, if somebody had a viable competitor, which would even be marginally cost competitive, if my guess is many of the big consumers of GPUs would immediately shift some revenue there, just to have a number tool, right?

32:23Just to just do that. That's AMD today, right? And Microsoft stopped. Yeah. I mean, like, there is still pre -limited traction, though. Sure, but yeah. Medic continues to buy from them. And Microsoft did buy a bunch, and then they stopped, because it's like, well, yes, they're, you know, AMD's giving you all these advantages, but ends up still not being better on a performance per lot basis and they have a way bigger software team. They're somewhat competitive on like all these dynamics that I mentioned, right? So you can't just like do the same thing as Nvidia. You really and do it better, right?

32:52Or try and execute better like AMD. Like you have to really leap forward in some other way. But that's the design cycle takes so long that models will shift, right? Because they're like, oh, what's the next inertia in TPU and GPU look like? Okay, let's optimize for that. And the research path is, you know, like great, like yes, neuromorphic computing could be the most optimal thing for us to do. But no one's working on that because you have to advance in the tech tree, you've chose it, right? If you restart the tech tree, you're going to be like, well, the sucks. And so like if it branches this way and you're over here, you're screwed.

33:25Because you have to be five x times as a mode. Because the supply chain stuff means that five x actually turns into a two and a half x. And then Nvidia can compress their margin a little bit if you're actually competitive. and then that two and a half X becomes like a 50 % better. And then yeah, so it's like it ends up being way too difficult to, and the software stuff, right? Everything like takes your 5X and makes it like, oh, you're actually only 50 % better. And defense supply chain for sure. Yeah, defense supply chain. And then like, they get that, right? Like so it's like, and let Nick himself said, we had to do this for Rural Thin Minerals.

33:57That's like interesting. China, there's like provinces in China that have like rules that say the H20 is not efficient enough to be deployed, which is like super bizarre because it's clearly the best AI chip China has. Paul is still a little bit behind. Well, what's interesting is that efficiency is just not, is so much less of an issue in China than here because they just have the power infrastructure to be able to support. So even if they're running less powerful chips, you know, you would imagine that it doesn't really matter because China has just such an infinite supply, infinite supply of power that they'd sort of be okay with it.

34:32So it's interesting. Which is a big challenge in America, right? Like, there have been companies that were like, they wouldn't, they'd like, you know, Johnson keeps saying he couldn't give away H20 in America for free. But I've literally like heard companies like say like now say like, yeah, no, I mean, I wouldn't because like, I only have this much power, how am I going to, you know, in data centers ready to go over the next year, if I bought an H20, I'd literally have less compute capacity and then I'd lose, right? Even if it was free, like it doesn't make sense. Whereas China doesn't care, they can build these things.

35:05They have the muscle. I'm curious how this all shakes out, you know, China's posturing really hard. They've been like put out something and I was like, we're investigating to see if there's back doors in the H20, it's like there's no back door in the H20 like chill. So, you know, it's like, you know, GPUs are usually like firewalled from the public internet anyways. Like you step through stuff before you get to the GPU clusters. So like a backdoor wouldn't even matter. I don't know, I think it'll be interesting to see because China can definitely deploy way, way, way more power to AI the moment they decide to.

35:46But there's these like, there's like competing interests, right? Like, because they want Huawei to be better than Nvidia. Yeah, and then this is how Nvidia argued to the administration. They're like, if we don't do this, I think it's like a very like powerful argument that like, like, for example, within Triton, which is a common ML library anyway, like, like bite dance has open source some stuff that plugs into this that is like super awesome. And there's like all these other libraries. It's not just models that China open sources. It's like software for Nvidia that Chinese companies open source.

36:18In a sense, like by Nvidia selling GPUs, Nvidia's argument again, like was like they were able to stop Huawei from building up a software ecosystem and the Western ecosystem is better. But then in the flip side, it's like, again, if you believe the models deliver more economic value to society than the hardware, which I actually think they do. It's just there's a value capture problem today. Then you're giving China way more by giving them H20s and soon a version of Blackwell that's cut down like Trump said, right? Versus, you know, selling in the chips, right? The economic value derived from selling in the chips is not as large as, you know, being able to somehow sell them AI services.

36:56So is China gatekeeping power for AI? I don't think so. I think, again, like there's a lot of like, What we see is that like even with H20 being sold to China into China and future versions of the chip, H20E and other chips, we still see like Chinese companies like Alibaba, renting GPUs outside of China because the GPUs they can get outside of China are just so much better on a dollar spend per performance basis, renting them or even like going through sort of like a Singaporean company that is effectively a Chinese company and building data centers and putting chips in them. So it's like, I don't think China's limiting the power per se.

37:40It's that it's, you know, you can only, if you can spend like your Chinese companies are growing their capex way more than US companies on a percentage basis next year. The dollar, absolute dollar number is, you know, obviously the US company are spending more still on AI. The percentage basis Chinese companies are growing more next year. And you, you still have the problem of like, well, dollars spend to AI output in tokens or in whatever is going to be lower because these chips are worse. So power is not the gating factor. It's always capital, right? At least today, right? Now, China can spend a lot more capital if they wanted to.

38:16They're subsidizing the semiconductor industry to the tune of like $152 ,200 billion a year through SOEs, through CapEx that's not generating revenue, et cetera. So it's not like they couldn't do this today, AI ecosystem, right? Given, you know, Metas CapEx is like 60 billion, right? and Google's capex is like 80 billion, right? Like they could totally spend way more than that on a single effort they just haven't decided to. And I just think for the US, our buildouts are constrained by power, right? Like Google has a ton of TPUs sitting waiting for data centers to be powered and ready. As does Meta with GPUs, right?

38:50We posted about how Meta's now building these like effectively tense. Is this just something we also coupled to the unwillingness to sell them to a broad ecosystem? system. I mean, if they want to be confined in their own data centers and they, you know, didn't ram data center build out for their own high for their own, so high basically, it's because it's quickly enough, right? And yes, that constrains them, right? If they were on the open market, we'll be still be constrained. Yeah, yeah, for sure, because like, companies like CoreWeve, you know, why is CoreWeve valuable is really because they build infrastructure really fast, right?

39:22And their software is nice, I think, but like a lot of their customers are bare model, right? Just just replace the GPUs whenever they're broken and networked. They think they grew more aggressively and I think I think they'll go anywhere. Jens and Lyson as well to be there. Yeah, they'll go. Yeah, yeah, that's very important as well. But they'll like go because it's it it it unconstitutes the ecosystem which is better for Nvidia. Right? Have you worked at it? I know exactly what you're going to as my. Yeah. So I think what's really important is that like core we have doesn't care, right? They're like, Oh, crypto data center.

39:50I will convert it to a AI data center, right? They bought a company for like $10 billion that's doing crypto mining, which is worth like $2 billion like a couple years ago. And it's not because they're Bitcoin mining businesses growing. It's because they have powered data centers, right? Like anywhere and everywhere, people are trying to build power data centers. And company is like like CoreWeave and Oracle are moving to the actually today. Google just didn't bought 8 % of a crypto mining company called Teril Wolf, right? Right. Yeah, because they're getting into crypto mining. No, because they need the data centers, right?

40:24They need the power, right? And it's like, all the hyper scalers have like said, screw off to my sustainability pledges because they need power as fast as possible, right? They're doing things that are not, that they take a little bit longer to move the ship, but like, even if you didn't do it in your own self -built data centers, there's still a lot of challenges in the open market. There's a deficit, right? And that's, that's constraining American chip buildouts heavily. Yes, others could maybe do it a little bit faster, like Corvieve or others, right? Oracle's got an open mind as well. But it's still constraining US buildouts heavily.

41:07Even though the capital has been spent, right? The chips are 60 to 80 % of the cost of the cluster, depending on what chips you're getting. So it's like, they've already bought the chips. They just can't put them anywhere because the data centers aren't ready. Supplies to Google, Pyson, Microsoft, PysDemeta, PysTo a lot of folks. I mean, it's really hard to build power in the US. Yeah. Power, greener connections, transmission, substations, all of this stuff. Electrical contractors, electricians, in Texas, if you're willing to be a travel electrician, it's like, it's like oil pay, right? It used to be that if you're physically adept, You could go make, you know, a hundred grand in West Texas, but like who the fuck wants to do that?

41:47Now it's like, well, you could go like 200 miles away from Dallas and what's still a reasonable town and build a data center and work on the wiring within the data center and all this other stuff, the transmission stuff, and your pay is up like 2x now versus what it was just a few years ago. This labor problems are challenged too. And it's, yeah, I think in China, they don't have any of these problems, but they just haven't spent the capital yet. but capital is an issue as well. Because of the scale of what's being spent, right? Like, in videos revenue this year is going to be like over $200 billion, and next year it expects over $300 billion, plus Google is going to spend like $50 billion on TPU data centers, right?

42:27And it's like, and Amazon's going to spend tons and tons on training data centers. It's like, the scale of dollars is quickly growing to nation state level stuff. And what's more important is being able to decide to spend the dollars in what's cost effective. And so to some extent, China still constrained by that, but they can smuggle chips in, they can build data centers outside of China, they can rent data centers outside of China, and have the most cost effective, blackwell chips or whatever, right? Bike dance is either the biggest or the second, biggest customer of Google Cloud for a reason, right?

43:03And they're getting, you know, over, you know, they're getting many, many blackwell from them, right? And the same with Oracle and the same with Microsoft often, all these other companies are renting tons of chips to China anyways, because it's more cost effective to do that than build it yourself. So it's not like China has this mentality where we only have to, well, the government does, but the infrastructure companies don't, like Alibaba, Tencent, Baikdance, etc. So what's the endgame for data centers? I mean, like we need more power, we need more cooling, well, the MPE, every, all data centers would be next to a nuclear reactor, lots of solar, you know, next to a deep level, like deep sea water that we use for cooling or something like that or what's the thing?

43:42I think that cooling is, like the physical cooling of a data center are like, you know, there's this little narrative about like, oh, yeah, I use this so much power and it's like not really, you know, farming else alpha uses like 100x the water of AI data centers, even by the end of the decade, it'll be the same. And it's like, alpha is like worth very little. So it's like, I don't know. There's like, it's like cooling is like not that. You know, people have like experimented with like, you know, undersea data centers to reduce the cooling cost. Yeah, that doesn't make sense. It's like 5, 10 % savings, but then like, if you were to get the water out of the ocean then, then with the data center, the ocean.

44:16And so if you want to service it like you're screwed, right? So like the same with power, it's like, we talk a lot about like, the power is not actually that expensive. It's just hard to build, right? How to get to the right place? I'm going to get to the right space and convert it down to the voltages and all the stuff that chips need. So it's less than magnitude of power and more where it is and how it moves. Well, the magnitude too, right? Like it's going to be. It does a total world wild energy consumption. AIJ does it is still the full. It's like, it's like nothing. Yeah, it's a fraction of a percent.

44:44It's not that. Yeah, yeah. Even by the end of the decade, you know, the US will be like 10 percent of our power will be AI data centers, which is still like electricity. Of our electricity. I know it's a bit of energy that's even a smaller fraction, right? Oh, yeah, yeah, because you think about. My shift into electric vehicles, I was like, you can probably make a bigger swing than and then, you know, with all the data centers, we can build with the data. But outside, like, it's like in Europe, like that number's not moving up that fast and like all these other countries, I think we need to build a lot more power, but it's not like some crazy, crazy amount.

45:16It's just like doing it properly is the hard thing. And again, like the cost of power, like, you go look at like these deals, people are signing, they're still signing, like even though the price is skyrocketed from like, if you sense a kilowatt hour for these massive, massive purchases to like 10. It's still, you know, when you think about the full TCL, the cluster, you know, the GPU cost of networking, all of this stuff far out, trips the power. Yeah. And same with cooling. But what percentage is power from like a V8, do a four year amortized GPU data center? What percentage will be power 80 % of the cost of a GPU data center if you're building black well is capital, right?

45:53It's the GPU purchases. It's the networking. It's the it's the physical data center conversion, conversion, power conversion equipment, all of this stuff is like 80 % of the cost. And then 20 % is going to be your lack and your power and your cooling and your cooling towers and your backup power and your generators and all this stuff. It's like nothing, which is why it doesn't matter if you spend, you know, 10 % or 50 % more on that. Because at the end of the day, the expensive thing, right? Like this is why what Elon did would seem silly, right? they spent a lot more money on generators outside the data center and these mobile chillers to cool the water down for their liquid cooling instead of like the more cost -effective option because it got the data center up three months faster.

46:37And so like that three months of additional training time is worth way, way, way more on a TCO basis, right? The performance you got out of the chips and the time to market and all this is way, way faster. And therefore, it was the right decision. Even though this part of the data center ballooned at cost, everything else is still there and you're still paying for the chips. And if they were sitting idle, it's not worth it. Right. Just by like bypassing the grid, bypassing anything to do with interconnect, anything to do with public utilities. Exactly. Exactly. What's your take on Intel? Where's Intel going?

47:07I think the world, well, the US needs Intel. I think the world needs Intel. I think the world needs Intel because like Samsung is doing worse than Intel on leading edge process development in my opinion based on even on various customers in the industry having done touch chips at like Intel versus Samsung. They think industry generally agrees that Intel is further along the sort of the two nanometer class process technology than Samsung is, but both are way behind TSMC. And TSMC is a monopoly in some extent. The number one question always people ask is like why is TSMC not making more money? Why are they only raising prices next year, you know, 3 to 10 percent depending on what it is.

47:48It's like, just some season monopoly. Like they could raise a lot more, but they're they're good Taiwanese people rather than like dirty American capitalists. So TSMC was owned or was managed by Americans. I think most ownership is actually American in terms of the stock, it's on the New York Stock Exchange and all this like, you know, they would have raised prices a lot more. And so like there is this like difficult difficult thing to be done that like, hey, there's one island that controls all leading edge semiconductors and not just all leading edge, like the majority of trailing edge production as well.

48:21Yeah. Something needs to be done until it's behind, but not like, not like absurdly so, right? Like if something were to happen to Taiwan, Intel would have the most advanced technology in the world, right? It's just, it's not economic. Can you keep Intel as one company if you want them to be competitive? I think the process of splitting it would take so much executive time and so much executive effort that you would have been bankrupt by then, right? And that's the big challenge. Like I think Intel should be separate, right? But to properly split the company and for all the management time that's needed, there's like absurd.

48:54And instead like what you need is like, you need Lip -Boutan, who's the CEO of Intel. You know, there's a lot of drama going around about him because he's one of the greatest semiconductor investors ever, right? He's invested in so many different companies first. You know, he was on the board of like SMIC, which is China's TSMC effectively, which is like a big like drama or like the, some of the biggest tool companies in Chinese, the first investor in them, because you know, there's a multi -polar world there and he's making good investments. But like, you know, now like people are getting mad about that but it's like, no, he recognizes he, the company's, like he understands the supply chain.

49:29He needs to not spend his time on splitting the company because then he never actually fixes the company, right? Intel's problem is that like, it takes them five to six years to go from design to shipping the product. in some cases more. And when they tape out a chip, right? Like, you send the design to the fab, the fab brings back the chip. They go through 14 revisions in some cases, where it was like the rest of the industry goes through like one to three, right? Revisions, if they're good. Of like send the design in, get the chip back, test it, send the design in, right? For a public launch.

50:03And they'll launch a chip in three years. So, but if you look at it until today, right? They still don't have a competitive entry on the AI side. And they what? Can you, so what does it mean for their offering? I mean, they're doing great on CPUs. They don't have a good AI, a Azure product. Is it long term sustainable positioning? Right? I mean, there's a standard on chip company here. I mean, IBM still makes more money every launch off of mainframes. So it's not like X86 is dead. It's like you don't get the growth rates, but like you could totally run this as a very profitable enterprise. And I think the same with PCs, right?

50:37There's some turmoil, there's some arm entry, there's some AMD competition. Well, I think it's a very, it can be a very profitable business if it had like one third the people or half the people working on it. And so like Lipputan to fix Intel needs to go into both the design company and lay off a shitload of people, but like keep all the good people and make sure that they're designing fast and they're launching from design conception to launches two to three years, not five to six. And that's on the design side and make that profitable. And then on the FABs, he has to do the same thing. There's all these people, like, one of the heads of FAB automation at Intel.

51:15I explicitly told Lip Bootan, because we have a couple of X Intel people who are actually good in the company that worked on the FAB side. And we're like, they were like, who's the worst people in France? It's like, oh, this guy sucks. I explicitly told Lip Bootan, he had never talked to the guy because it was like, four layers down. The company has like absurd amounts of hierarchy. It's like four layers down. He goes to talk to the guy and he's out, right? it's like, he figures out who's bad, right? And who's good? And he has to go in and he's still like, hey, the vast majority of the team at Intel is the one who led the world in production and process technology for 20 years.

51:48But there's a lot of built up crap. So he has to go figure this out. He can't waste his time on all this structuring to split. I think it would be better if the company split. I just don't think he can spend the time to do that. And if the design side of the company is, you know, you're not really going to get into AI, you're not really going to, you have to make some money there. But the fabs, I think, could truly become a competitor. But they're going to go bankrupt by the time anything can happen. So they have to figure out how to get capital. So he has to figure out how to get capital. He has to figure out how to clean up all the crap.

52:21Make the yields go up, right? Make the product ship way faster. Like all of these things are basically - I think the goals are completely correct. I mean, I think the big challenge is just for a vacuum back in my time there, right? I think the big challenges that right now, if you look at Intel, right, they have essentially software, software, the chip design making it, and then there's something to the core manufacturing part, right? And they have three different, very different cultures. And it's very hard to get everything on the one on the brother, right? And so I think that is the big challenge.

52:48I think you should do that run the company separately, right? But like, you can't physically separate them entity -wise because it's going to take so long to sever all these things, because he doesn't have time, right? like Intel is literally going to go bankrupt if they don't have a big cash infusion, or they lay off like half the company, right? Which some could argue you need to lay off like 30 % of the company anyways, but there's a lot of bad things that happen if that happens, right? And they need to spend a lot more on building the next generation fat, even if they fix the fat. And they don't have money for that, right?

53:19So there's like, there's like a lot more more important problems than like physically separating the company, even though I think long -term, Yes, the fab has to be separate from the chips design software, right? Like our chip design part of the company just like that's going to make each company much more accountable Be able to service their customers better etc It's just that's going to take too long and they're going to go bankrupt by then Awesome I but I think I think I hope I hope I pray Someone does something right like you get a big capital infusion I don't know the big hyper scalers are like muscled into like oh, okay Wait if TSMC eventually grows their margin to 75 % because of the monopoly plus they intake all this stuff, like co -package optics and power delivery and all this, like all of a sudden the cost is gonna spike.

54:01So we should actually just throw $5 billion at Intel each, right? Screw it. And that could actually give Intel enough of a lifeline to potentially get to something and maybe be competitive. That's the hope. Can we finish by finishing this game that we started when we gave Sam and Altman advice if Jensen was here, what advice would you have from? If Jensen was here, I think he has a massive, massive balance sheet, right? Jensen does. He's cash -free cash flow is ridiculous. The tax cut, the new Trump tax bill institutes something really incredible, which is that you can depreciate all of the GPU cluster cost in your one, which we put out like a note about how the tax implications to meta are 10 billion dollars a year.

54:53And across each of the major hyperscalers, it's massive. It's like, well, Nvidia is going to spend tons and tons of cash, or they're going to spend tens of billions of dollars of taxes. Why don't you get into the infrastructure game somehow? Now this is obviously going to be crazy because now they're buying GPUs and putting them in data centers and doing stuff and they're competing with their own customers, but they're already doing that anyways because their customers are trying to make chips. But they should accelerate the data center ecosystem with investments, right? Because really, we think we can have very high degree of accuracy on what they're going to do next year in terms of revenue because it's just the number of data center watts that are being built, right?

55:35This is harder thing to shift up and down, right? Now there's a little bit of share difference between how much is TPU versus TPU, but you You have to accelerate the infrastructure and you need to spend all of this capital that you're building, right? Like, okay, do you want to go the route of like doing buybacks and dividends? Like, great. Like, you're a loser if you do that, right? Like you can make more money by reinvesting and building a bigger company that's not just chips into the ecosystem or servers into the ecosystem, but actually like controlling the infrastructure end to end somehow.

56:05So I think there's something he could do there with this massive war chest. And there was a reason like Nvidia has done some buybacks and they've done some dividends and increasing, but the cash on their balance sheet keeps growing and they're going to have north of a hundred billion dollars of cash on their balance sheet by the end of this year, I think. So it's like, what are you going to do with that? I think there's something moving into the infrastructure layer much more that they could do if you really wants to be the king of the world, right? Which I think he does. As Sergei and Sender?

56:39I think they should open up the Commodo on TPs, right? Like start selling them, open up the software, open source a lot more of the XLA software because there's open XLA and there's XLA but the vast majority of the closed source, really, really open up the Commodo on that and be a lot more aggressive, right? They're still pretty not aggressive on data centers. They're pretty not aggressive on a lot of elements to the company. The TPU team's next -gen designs are pretty not aggressive, partially because a lot of the TPU team has left to go to OpenAI, the best people that I knew. It was actually really annoying.

57:15I knew like four people or five people and they all went to OpenAI and it's like, fuck, like, now I don't get as much. I met some other people, right? But it's like, you know, I think they could be a lot more aggressive in many ways across the company. They don't have to be, right? But they could. Because AI, you know, like this Chad GBT take gray, the shift of search queries, the monetizable ones, especially from two purchasing agents is going to really screw Google long -term if they don't get their act together. I think they've gotten their act together on DeepMind. There's still some inefficiencies, but Sergey works on, works with DeepMind a lot in their driving hard.

57:50There's still a little bit behind, but like, I think like physical infrastructure, TPU, and how much money they could make, and how much they could take the wind out of everyone and else's sales if they start selling TPUs externally and reorg around like building data centers much faster so that they do have the most compute in the world because they did. But now there's certain companies that are gonna surpass them potentially over the next few years if they don't really get their act together. So I think that's what I would say for them. Yeah, and also like learn how to ship product. Yeah, I think you should.

58:23Zach. I think, I think, Zuck, you know, it remains to be seen what goes on with super intelligence, but like they're trying to move super fast with the data centers, you know, like screw it, we'll build tents instead of like physical data centers because we only need these for five years anyways. You know, the super intelligence moves, you could, you could say whatever you want, but like, you know, trying to buy like thinking for like 30 billion or SSI for 30 billion didn't work out. So then they spent, you know, not even that much on higher, not 30 billion on hiring all these people. So I think that he recognizes the urgency with the models, with the infrastructure.

58:59So I really think he needs to like, you know, if you read his website post about like AI, like I think, you know, he sees the vision, right? There's the wearables, there's integrating AI into that, there's being your AI assistant to do all this purchasing and stuff. I think he sees the vision, but I think he also needs to focus on like actually like releasing that faster, but also like the products that they do outside of their core IP, every time they launch something is kind of mid, right? You know, metal reality labs is doing well, but I think they should like go more explicit, like have a chat, GPD competitor, have a cloud, like cloud code competitor, like just start releasing way more products because they're really just focused on their individual gardens rather than like branching outside of it.

59:46Do you think apples should have that same sense of urgency or 10 cook was here? What would you tell them? The funny thing is like some of their pissed AI people are now like at super intelligence. They're building an AI accelerator. They're going to they're they're have AI models, but they're just like way slower. They did mention on the last earnings call they're going to allocate more capital to this, but it's like, guys, Apple, like you guys are going to lose the boat if you do not spend like $50 $100 billion on infrastructure. You don't think the kind of we'll cut it. I think like more and more you'll see people like, you know, great Apple has this world garden, but like they can only do so much to protect it, right?

1:00:23IDFA, like they shut down ads to or data sharing to meta, but meta made better models. And now they have way more data and way more power over the user than they ever did before. Kind of it was good that meta kicked the crutch off of them or Apple did. But the same applies to like AI, like yes, they have access to the text and they have access to this. But like, I think other people are going to be able to integrate user data. And agents will be able to integrate all this user data. And they'll start to lose control of what the user experience is. As more and more gets disinheredated by AI being interface rather than touch, rather than touch pad and keyboard.

1:01:00And I don't think they've truly realized what happens when the interface to computing is AI. Like they market it, but like that's going to shift computing really heavily. they have great hardware and their hardware teams are working on awesome stuff and form factors, but like I just don't know if they like get what is actually going to happen to the world in the next five years truly well enough and they're not building fast enough for it. What about Microsoft to that end? Microsoft has the same problem I think they were super aggressive in 23 and 24 and then they pulled back heavily right now like opening eyes slipping through their their graphs, there's that whole thing there.

1:01:40They cut back on data center investments heavily. They were gonna be the largest infrastructure company in the world by like a factor of 2x, which would have been, you know, you could argue maybe that was like too much and maybe it wouldn't have been economical, but like they're losing grasp on open AI, their internal model efforts are failing, spectacularly like they're on LLM arena right now and they're pretty decent there, but it's like that's just like a sycophantic model. Like it's a code name, but like whatever. Like, M .A .I. is like failing, Azure is like losing a lot of share to Oracle and Core Weave and Google and so on and so forth, right?

1:02:16Their internal chip, chip effort is by far the worst of any hyperscaler. Like, they're just like, mis -executed. Like GitHub, how does GitHub not the highest ARR software code model? I mean, they only had the best IDE, the best source code repository, the best enterprise I say it's for us, the best model company as a relationship and they're by the first market. It's like they're never going for them. And like there's just nothing, right? It's like, get up, get up, get up, co -pilot is failing. Microsoft co -pilot is like still crap, right? Like, it's unusable. It's like, what is, what is, you know, what is going on?

1:02:51Like you did a shake to crap out of the company. Like, I think they win a lot because they have the best business to business relationship with so many enterprises. That's why it's for us on the planet. Yeah, but like they end up like not having actual product to sell them, which is like really scary So they need to really work on product. Yeah, such such as done great on sales and stuff But like yeah, if Elon was here, what advice would you have him? A lot of people at XAI are mad about the porn models like porn porn stuff It's fine like you're gonna make a ton of money off of this This is how you accelerate the revenue of that company But like he's losing a lot of talent and and acting a lot of good projects but Elon has is a magnet to amazing talent and building stuff so I won't bet against him but it seems like since he left the administration and focused on stuff again.

1:03:35But I think I don't know, I think he's focused on a lot of things and I think like Robo Taxi, starting to look good actually again. Like I haven't ridden one yet but I have some friends who've ridden one. It's like looks pretty decent. He could like not make these snap decisions which often are the reason why he's amazing. But like some of these snap decisions are hurting him. I'm not sure if I can give Elon that much great advice because I think maybe it's just like focus on like the products again, right? More. But he is working on that stuff a lot. Yeah. I think that might be a good place to wrap.

1:04:05Awesome. It's a great discussion. Don't thank so much for joining us. Thank you for having me. Thanks for listening to the A16z podcast. If you enjoyed the episode, let us know by leaving a review at rate this podcast dot com slash A16z. We've got more great conversations coming your way. See you next time. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the company's discussed in this podcast.

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